Research Article
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AUTOMATED PSYCHIATRIC DATA ANALYSIS from SINGLE CHANNEL EEG with SIGNAL PROCESSING and ARTIFICIAL INTELLIGENCE METHODS

Year 2022, Issue: 050, 106 - 123, 30.09.2022

Abstract

Artificial Intelligence (AI) methods have been generally used in neuroimaging data to identify patients with psychiatric problems/disorders. Schizophrenia (SZ) is generally defined as a mental problem that affects the thinking ability and memory. Manual assessment of SZ participants is sometimes difficult and susceptible to diagnostic mistakes. Thus, we achieved a Computer Aided Diagnosis (CAD) algorithm to analyze and interpretate SZ patients successfully using single channel measurement Electroencephalogram (EEG) signals with Signal Processing and Artificial Intelligence methods. First, the EEG signals of participants were pre-processed (signal enhancement, filtering, noise removal), Then, signals were disseminated into windowing/segmentation process. Then, the EEG signals are separated with wavelet decomposition via seven sub-bands. Next, the feature extraction process was achieved and specific feature parameters were obtained by summing the numerical values of the processed signals. Then, Feature ranking process was achieved to identify the obtained features of the normal and schizophrenia groups. After ranking process, features are fed to AI (SVM), We have obtained the highest accuracy of 99.31% using SVM with five fold and take off one cross validations.

Thanks

This study was completely achieved and prepared by Dr. Ali Berkan URAL.Indeed, we thanked Dr. Uğur Eray for data analysis, labeling and evaluation processes for AI training part.

References

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  • [11] Santos-Mayo L., San-Jose-Revuelta L.M., Arribas J., (2016), A computer-aided diagnosis system with EEG based on the p3b wave during an auditory odd-ball task in schizophrenia, IEEE Trans Biomed Eng, 64(2):395–407.
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  • [15] Vicnesh J., Oh S.L., Rajinikanth V., Ciaccio E., Cheong K., Arunkumar Acharya U.R., (2019), Automated detection of schizophrenia using nonlinear signal processing methods, Artif Intell Med 100:101698.
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  • [19] Li Y.M., Wei D., Zhang L., (2021), Double-encrypted watermarking algorithm based on cosine transform and fractional Fourier transform in invariant wavelet domain, Information Sciences, 551:205-227.
  • [20] Gannouni S., Aledaily A., Belwafi K., Aboasamh H., (2021), Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification, Sci Rep, 11, 7071. https://doi.org/10.1038/s41598-021-86345-5
  • [21] Biswas S., Maniruzzaman M. and Bairagi R.N., (2021), Noise Removing from ECG Signal Using FIR Filter with Windowing Techniques, 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 1-4, doi: 10.1109/ICECIT54077.2021.9641381.
  • [22] Yu-xing L., Shang-bin J., Xiang G., (2021), A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy, Defence Technology, 17(5): 1625-1635.
  • [23] Yong L., Gang C., Chang L., (2021), Research on bearing fault diagnosis based on spectrum characteristics under strong noise interference, Measurement, 169:108509.
  • [24] Havryliuk V., (2019), The Wavelet Based Detecting of the Signalling Relay Armature Defects, 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON),
  • [25] Borowska M., (2015), Entropy-Based Algorithms in the Analysis of Biomedical Signals, Studies in Logic, Grammar and Rhetoric.
  • [26] Kaya Y., Kuncan M., Kaplan K., Minaz M.R. and Ertunç H.M., (2021), A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification. Journal of Experimental and Theoretical Artificial Intelligence, 33(1):161-178, Doi: 10.1080/0952813x.2020.1735530
  • [27] Ribeiro M., Henriques T., sa Castro L., Souto A., s Antunes L., Costa-Santos C., Teixeira A., (2021), The Entropy Universe, Entropy.
  • [28] Yunqiang Z. Guoquan R., Dinghai W., Huaiguang W., (2021), Rolling bearing fault diagnosis utilizing variational mode decomposition based fractal dimension estimation method, Measurement, 181: 109614.
  • [29] Humairani A., Atmojo B.S., Wijayanto I., Hadiyoso S., (2021), Fractal based Feature Extraction Method for Epileptic Seizure Detection in Long Term RRG Recording, J. Phys. Conf. Ser., 1844 012019.
  • [30] Petropulu A.P., (1994), Higher-Order Spectra in Biomedical Signal Processing, IFAC Proceedings Volumes.
  • [31] Baygin M., Dogan S., Tuncer T., Prabal D.B., Oliver F., Arunkumar N., Enas W.A., Elizabeth E.P., Acharya U.R., (2021), Automated ASD detection using hybrid deep lightweight features extracted from EEG signals, Computers in Biology and Medicine, 134:104548.
  • [32] Kavitha N., Soundar R., Kumar T., (2021), An Improved DFA Based Kernel Ensemble Learning Machine Using Local Feature Representations for Face Recognition, Journal of IntelligentandFuzzy Systems, 1203 – 1216.
  • [33] Jian L., Zijian Q., Xiaojian D., Bing H., Chuanlai Z., (2021), Stochastic resonance induced weak signal enhancement over controllable potential-well asymmetry, Chaos, Solitons and Fractals, 146:110845.
  • [34] Yuanhang S., Jianbo Y., (2022), Adaptive adjacent signal difference lasso for bearing fault detection, Measurement, 190:110652.
  • [35] Ural A.B., Eray U., (2022), Psychiatric Data Analysis And Interpretation With Artificial Intelligence, Machine Learning And Deep Learning, ISPEC Publication New Horizons İn The Health Sciences, 231-256.
  • [36] Ural A.B., (2021), Deep Computer Based Pre-Diagnosis From Chest CTs of COVID-19 Patients, 2021 13th International Conference on Electrical and Electronics Engineering (ELECO), 229-233. doi: 10.23919/ELECO54474.2021.9677723
Year 2022, Issue: 050, 106 - 123, 30.09.2022

Abstract

References

  • [1] Zayrit S., Belhoussine D. T., Nsiri B., Korkmaz Y., Ammoumou A., (2021), The detection of Parkinson disease using the genetic algorithm and SVM classifier, Applied Acoustics 171, 107528.
  • [2] Jarchi D, Andreu-Perez J, Kiani M, Vysata O, Kuchynka J, Prochazka A, Sanei S., (2020), Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning, Sensors, 20(9):2594. https://doi.org/10.3390/s20092594
  • [3] Ipsit V.V., Zachary K., Chen-Yu H., Brent P.F., Patrick M., Rose M., Katherine H., Usman M., Kreshnik H., Dina K., (2020), Radio Signal Sensing and Signal Processing to Monitor Behavioral Symptoms in Dementia: A Case Study, The American Journal of Geriatric Psychiatry, 28(8):820-825.
  • [4] Geman O., Chiuchisan I., Covasa M., Konstantinos E., Saeid S., Jonni G.F.M., Ronney A.M.B., (2016), Joint EEG — EMG signal processing for identification of the mental tasks in patients with neurological diseases, 2016 24th European Signal Processing Conference (EUSIPCO), 1598-1602. doi: 10.1109/EUSIPCO.2016.7760518.
  • [5] [5] Bone D., Lee C., Chaspari T., Gibson J. and Narayanan S., (2017), Signal Processing and Machine Learning for Mental Health Research and Clinical Applications [Perspectives], IEEE Signal Processing Magazine, 34(5):196-195. doi: 10.1109/MSP.2017.2718581.
  • [6] Faro A., Giordano D., Pennisi M., Scarciofalo G., Spampinato C., Tramontana F., (2005), Transcranial Magnetic Stimulation (TMS) to Evaluate and Classify Mental Diseases Using Neural Networks, In: Miksch S., Hunter J., Keravnou E.T. (eds) Artificial Intelligence in Medicine. AIME 2005, Lecture Notes in Computer Science 3581. https://doi.org/10.1007/11527770_43
  • [7] Valenza G., Garcia R.G., Citi L., Scilingo E.P., Tomaz C.A. and Barbieri R., (2015), Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics, Front. Physiol, 6:74. doi: 10.3389/fphys.2015.00074
  • [8] Zung, W.W. (1965), A self-rating depression scale. Arch. Gen. Psychiatry, 12:63–70. doi: 10.1001/archpsyc.1965.01720310065008
  • [9] Kim J.W., Lee Y.S, Han D.H., Min K.J., Lee J., Lee K., (2015), Diagnostic utility of quantitative EEG in un-medicated schizophrenia, Neurosci Lett, 589:126–131.
  • [10] Dvey-Aharon Z., Fogelson N., Peled A., Intrator N., (2015), Schizophrenia detection and classification by advanced analysis of EEG recordings using a single electrode approach, PLoS ONE, 10(4):e0123033.
  • [11] Santos-Mayo L., San-Jose-Revuelta L.M., Arribas J., (2016), A computer-aided diagnosis system with EEG based on the p3b wave during an auditory odd-ball task in schizophrenia, IEEE Trans Biomed Eng, 64(2):395–407.
  • [12] Iba n˜ez-Molina A.J., Lozano V., Soriano M.F., Aznarte J.I., Go mez-Ariza C.J., Bajo M., (2018), Eeg multiscale complexity in schizophrenia during picture naming, Front Physiol, 9:1213
  • [13] Abásolo D., Hornero R., Gómez C., García M., López M., (2006), Analysis of EEG background activity in Alzheimer's disease patients with Lempel–Ziv complexity and central tendency measure, Medical Engineering and Physics, 28(4): 315-322.
  • [14] Oh S.L., Vicnesh J., Ciaccio E.J., Yuvaraj R., Acharya U.R., (2019), Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals, Appl Sci, 9(14):2870.
  • [15] Vicnesh J., Oh S.L., Rajinikanth V., Ciaccio E., Cheong K., Arunkumar Acharya U.R., (2019), Automated detection of schizophrenia using nonlinear signal processing methods, Artif Intell Med 100:101698.
  • [16] Sharma M., Acharya U.R., (2021), Automated detection of schizophrenia using optimal wavelet-based 𝑙1 norm features extracted from single-channel, EEG. Cogn Neurodyn, 15, 661–674. https://doi.org/10.1007/s11571-020-09655-w
  • [17] Moulin P., Anitescu M., Kortanek K.O., Potra F.A., (1997), The role of linear semi-infinite programming in signal-adapted qmf bank design, IEEE Trans Signal Process, 45(9):2160–2174.
  • [18] Meignen, S., Pham D.H., Colominas M.A., (2021), On the use of short-time fourier transform and synchrosqueezing-based demodulation for the retrieval of the modes of multicomponent signals, Signal Processing, 178:107760.
  • [19] Li Y.M., Wei D., Zhang L., (2021), Double-encrypted watermarking algorithm based on cosine transform and fractional Fourier transform in invariant wavelet domain, Information Sciences, 551:205-227.
  • [20] Gannouni S., Aledaily A., Belwafi K., Aboasamh H., (2021), Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification, Sci Rep, 11, 7071. https://doi.org/10.1038/s41598-021-86345-5
  • [21] Biswas S., Maniruzzaman M. and Bairagi R.N., (2021), Noise Removing from ECG Signal Using FIR Filter with Windowing Techniques, 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 1-4, doi: 10.1109/ICECIT54077.2021.9641381.
  • [22] Yu-xing L., Shang-bin J., Xiang G., (2021), A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy, Defence Technology, 17(5): 1625-1635.
  • [23] Yong L., Gang C., Chang L., (2021), Research on bearing fault diagnosis based on spectrum characteristics under strong noise interference, Measurement, 169:108509.
  • [24] Havryliuk V., (2019), The Wavelet Based Detecting of the Signalling Relay Armature Defects, 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON),
  • [25] Borowska M., (2015), Entropy-Based Algorithms in the Analysis of Biomedical Signals, Studies in Logic, Grammar and Rhetoric.
  • [26] Kaya Y., Kuncan M., Kaplan K., Minaz M.R. and Ertunç H.M., (2021), A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification. Journal of Experimental and Theoretical Artificial Intelligence, 33(1):161-178, Doi: 10.1080/0952813x.2020.1735530
  • [27] Ribeiro M., Henriques T., sa Castro L., Souto A., s Antunes L., Costa-Santos C., Teixeira A., (2021), The Entropy Universe, Entropy.
  • [28] Yunqiang Z. Guoquan R., Dinghai W., Huaiguang W., (2021), Rolling bearing fault diagnosis utilizing variational mode decomposition based fractal dimension estimation method, Measurement, 181: 109614.
  • [29] Humairani A., Atmojo B.S., Wijayanto I., Hadiyoso S., (2021), Fractal based Feature Extraction Method for Epileptic Seizure Detection in Long Term RRG Recording, J. Phys. Conf. Ser., 1844 012019.
  • [30] Petropulu A.P., (1994), Higher-Order Spectra in Biomedical Signal Processing, IFAC Proceedings Volumes.
  • [31] Baygin M., Dogan S., Tuncer T., Prabal D.B., Oliver F., Arunkumar N., Enas W.A., Elizabeth E.P., Acharya U.R., (2021), Automated ASD detection using hybrid deep lightweight features extracted from EEG signals, Computers in Biology and Medicine, 134:104548.
  • [32] Kavitha N., Soundar R., Kumar T., (2021), An Improved DFA Based Kernel Ensemble Learning Machine Using Local Feature Representations for Face Recognition, Journal of IntelligentandFuzzy Systems, 1203 – 1216.
  • [33] Jian L., Zijian Q., Xiaojian D., Bing H., Chuanlai Z., (2021), Stochastic resonance induced weak signal enhancement over controllable potential-well asymmetry, Chaos, Solitons and Fractals, 146:110845.
  • [34] Yuanhang S., Jianbo Y., (2022), Adaptive adjacent signal difference lasso for bearing fault detection, Measurement, 190:110652.
  • [35] Ural A.B., Eray U., (2022), Psychiatric Data Analysis And Interpretation With Artificial Intelligence, Machine Learning And Deep Learning, ISPEC Publication New Horizons İn The Health Sciences, 231-256.
  • [36] Ural A.B., (2021), Deep Computer Based Pre-Diagnosis From Chest CTs of COVID-19 Patients, 2021 13th International Conference on Electrical and Electronics Engineering (ELECO), 229-233. doi: 10.23919/ELECO54474.2021.9677723
There are 36 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Ali Berkan Ural 0000-0001-5176-9280

Uğur Eray 0000-0001-5417-3394

Publication Date September 30, 2022
Submission Date May 1, 2022
Published in Issue Year 2022 Issue: 050

Cite

IEEE A. B. Ural and U. Eray, “AUTOMATED PSYCHIATRIC DATA ANALYSIS from SINGLE CHANNEL EEG with SIGNAL PROCESSING and ARTIFICIAL INTELLIGENCE METHODS”, JSR-A, no. 050, pp. 106–123, September 2022.